Tag Archives: memristors

A nontraditional artificial synaptic device and roadmap for Chinese research into neuromorphic devices

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A November 9, 2022 Science China Press press release on EurekAlert announces a new approach to developing neuromorphic (brainlike) devices,

Neuromorphic computing is an information processing model that simulates the efficiency of the human brain with multifunctionality and flexibility. Currently, artificial synaptic devices represented by memristors have been extensively used in neural morphological computing, and different types of neural networks have been developed. However, it is time-consuming and laborious to perform fixing and redeploying of weights stored by traditional artificial synaptic devices. Moreover, synaptic strength is primarily reconstructed via software programming and changing the pulse time, which can result in low efficiency and high energy consumption in neural morphology computing applications.

In a novel research article published in the Beijing-based National Science Review, Prof. Lili Wang from the Chinese Academy of Sciences and her colleagues present a novel hardware neural network based on a tunable flexible MXene energy storage (FMES) system. The system comprises flexible postsynaptic electrodes and MXene nanosheets, which are connected with the presynaptic electrodes using electrolytes. The potential changes in the ion migration process and adsorption in the supercapacitor can simulate information transmission in the synaptic gap. Additionally, the voltage of the FMES system represents the synaptic weight of the connection between two neurons.

Researchers explored the changes of paired-pulse facilitation under different resistance levels to investigate the effect of resistance on the advanced learning and memory behavior of the artificial synaptic system of FMES. The results revealed that the larger the standard deviation, the stronger the memory capacity of the system. In other words, with the continuous improvement of electrical resistance and stimulation time, the memory capacity of the artificial synaptic system of FMES is gradually improved. Therefore, the system can effectively control the accumulation and dissipation of ions by regulating the resistance value in the system without changing the external stimulus, which is expected to realize the coupling of sensing signals and storage weight.

The FMES system can be used to develop neural networks and realize various neural morphological computing tasks, making the recognition accuracy of handwritten digit sets reach 95%. Additionally, the FMES system can simulate the adaptivity of the human brain to achieve adaptive recognition of similar target data sets. Following the training process, the adaptive recognition accuracy can reach approximately 80%, and avoid the time and energy loss caused by recalculation.

“In the future, based on this research, different types of sensors can be integrated on the chip to further realize multimodal sensing computing integrated architecture.” Prof. Lili Wang stated, “The device can perform low-energy calculations, and is expected to solve the problems of high write noise, nonlinear difference, and diffusion under zero bias voltage in certain neural morphological systems.”

Here’s a link to and a citation for the paper,

Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices by Shufang Zhao, Wenhao Ran, Zheng Lou, Linlin Li, Swapnadeep Poddar, Lili Wang, Zhiyong Fan, Guozhen Shen. National Science Review, Volume 9, Issue 11, November 2022, nwac158, EOI: https://doi.org/10.1093/nsr/nwac158 Published: 13 August 2022

This paper is open access.

The future (or roadmap for) of Chinese research on neuromorphic engineering

While I was trying (unsuccessfully) to find a copy of the press release on the issuing agency’s website, I found this paper,

2022 roadmap on neuromorphic devices & applications research in China by Qing Wan, Changjin Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Peng Zhou, LinChen, Tian-Yu Wang, Yi Li, Kanhao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Z. T. Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang, Zheng Yu Ren, Li Qiang Zhu, Jianyu Du, Chen Ge, Yang Liu, Guanglong Ding, Ye Zhou, Su-Ting Han, Guosheng Wang, Xiao Yu, Bing Chen, Zhufei Chu, Lunyao Wang, Yinshui Xia, Chen Mu, Feng Lin, Chixiao Chen, Bojun Cheng, Yannan Xing, Weitao Zeng, Hong Chen, Lei Yu, Giacomo Indiveri and Ning Qiao. Neuromorphic Computing and Engineering DOI: 10.1088/2634-4386/ac7a5a *Accepted Manuscript online 20 June 2022 • © 2022 The Author(s). Published by IOP Publishing Ltd

The paper is open access.

*From the IOP’s Definitions of article versions: Accepted Manuscript is ‘the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP and/or its licensors’.*

This is neither the published version nor the version of record.

Studying quantum conductance in memristive devices

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A September 27, 2022 news item on phys.org provides an introduction to the later discussion of quantum effects in memristors,

At the nanoscale, the laws of classical physics suddenly become inadequate to explain the behavior of matter. It is precisely at this juncture that quantum theory comes into play, effectively describing the physical phenomena characteristic of the atomic and subatomic world. Thanks to the different behavior of matter on these length and energy scales, it is possible to develop new materials, devices and technologies based on quantum effects, which could yield a real quantum revolution that promises to innovate areas such as cryptography, telecommunications and computation.

The physics of very small objects, already at the basis of many technologies that we use today, is intrinsically linked to the world of nanotechnologies, the branch of applied science dealing with the control of matter at the nanometer scale (a nanometer is one billionth of a meter). This control of matter at the nanoscale is at the basis of the development of new electronic devices.

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A September 27, 2022 Istituto Nazionale di Ricerca Metrologica (INRIM) press release (summary, PDF, and also on EurekAlert), which originated the news item, provides more information about the research,

Among these, memrisistors are considered promising devices for the realization of new computational architectures emulating functions of our brain, allowing the creation of increasingly efficient computation systems suitable for the development of the entire artificial intelligence sector, as recently shown by INRiM researchers in collaboration with several international universities and research institutes [1,2].

In this context, the EMPIR MEMQuD project, coordinated by INRiM, aims to study the quantum effects in such devices in which the electronic conduction properties can be manipulated allowing the observation of quantized conductivity phenomena at room temperature. In addition to analyzing the fundamentals and recent developments, the review work “Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications” recently published in the prestigious international journal Advanced Materials (https://doi.org/10.1002/adma.202201248) analyzes how these effects can be used for a wide range of applications, from metrology to the development of next-generation memories and artificial intelligence.

Here’s a link to and a citation for the paper,

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications by Gianluca Milano, Masakazu Aono, Luca Boarino, Umberto Celano, Tsuyoshi Hasegawa, Michael Kozicki, Sayani Majumdar, Mariela Menghini, Enrique Miranda, Carlo Ricciardi, Stefan Tappertzhofen, Kazuya Terabe, Ilia Valov. Advanced Materials Volume 34, Issue32 August 11, 2022 2201248 DOI: https://doi.org/10.1002/adma.202201248 First published: 11 April 2022

This paper is open access.

You can find the EMPIR (European Metrology Programme for Innovation and Research) MEMQuD (quantum effects in memristive devices) project here, from the homepage,

Memristive devices are electrical resistance switches that couple ionics (i.e. dynamics of ions) with electronics. These devices offer a promising platform to observe quantum effects in air, at room temperature, and without an applied magnetic field. For this reason, they can be traced to fundamental physics constants fixed in the revised International System of Units (SI) for the realization of a quantum-based standard of resistance. However, as an emerging technology, memristive devices lack standardization and insights into the fundamental physics underlying its working principles.

The overall aim of the project is to investigate and exploit quantized conductance effects in memristive devices that operate reliably, in air and at room temperature. In particular, the project will focus on the development of memristive model systems and nanometrological characterization techniques at the nanoscale level of memristive devices, in order to better understand and control the quantized effects in memristive devices. Such an outcome would enable not only the development of neuromorphic systems but also the realization of a standard of resistance implementable on-chip for self-calibrating systems with zero-chain traceability in the spirit of the revised SI.

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I’m starting to see mention of ‘neuromorphic computing’ in advertisements (specifically a Mercedes Benz car). I will have more about these first mentions of neuromorphic computing in consumer products in a future posting.

Guide for memristive hardware design

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An August 15 ,2022 news item on ScienceDaily announces a type of guide for memristive hardware design,

They are many times faster than flash memory and require significantly less energy: memristive memory cells could revolutionize the energy efficiency of neuromorphic [brainlike] computers. In these computers, which are modeled on the way the human brain works, memristive cells function like artificial synapses. Numerous groups around the world are working on the use of corresponding neuromorphic circuits — but often with a lack of understanding of how they work and with faulty models. Jülich researchers have now summarized the physical principles and models in a comprehensive review article in the renowned journal Advances in Physics.

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An August 15, 2022 Forschungszentrum Juelich press release (also on EurekAlert), which originated the news item, describes two papers designed to help researchers better understand and design memristive hardware,

Certain tasks – such as recognizing patterns and language – are performed highly efficiently by a human brain, requiring only about one ten-thousandth of the energy of a conventional, so-called “von Neumann” computer. One of the reasons lies in the structural differences: In a von Neumann architecture, there is a clear separation between memory and processor, which requires constant moving of large amounts of data. This is time and energy consuming – the so-called von Neumann bottleneck. In the brain, the computational operation takes place directly in the data memory and the biological synapses perform the tasks of memory and processor at the same time.

In Jülich, scientists have been working for more than 15 years on special data storage devices and components that can have similar properties to the synapses in the human brain. So-called memristive memory devices, also known as memristors, are considered to be extremely fast, energy-saving and can be miniaturized very well down to the nanometer range. The functioning of memristive cells is based on a very special effect: Their electrical resistance is not constant, but can be changed and reset again by applying an external voltage, theoretically continuously. The change in resistance is controlled by the movement of oxygen ions. If these move out of the semiconducting metal oxide layer, the material becomes more conductive and the electrical resistance drops. This change in resistance can be used to store information.

The processes that can occur in cells are very complex and vary depending on the material system. Three researchers from the Jülich Peter Grünberg Institute – Prof. Regina Dittmann, Dr. Stephan Menzel, and Prof. Rainer Waser – have therefore compiled their research results in a detailed review article, “Nanoionic memristive phenomena in metal oxides: the valence change mechanism”. They explain in detail the various physical and chemical effects in memristors and shed light on the influence of these effects on the switching properties of memristive cells and their reliability.

“If you look at current research activities in the field of neuromorphic memristor circuits, they are often based on empirical approaches to material optimization,” said Rainer Waser, director at the Peter Grünberg Institute. “Our goal with our review article is to give researchers something to work with in order to enable insight-driven material optimization.” The team of authors worked on the approximately 200-page article for ten years and naturally had to keep incorporating advances in knowledge.

“The analogous functioning of memristive cells required for their use as artificial synapses is not the normal case. Usually, there are sudden jumps in resistance, generated by the mutual amplification of ionic motion and Joule heat,” explains Regina Dittmann of the Peter Grünberg Institute. “In our review article, we provide researchers with the necessary understanding of how to change the dynamics of the cells to enable an analog operating mode.”

“You see time and again that groups simulate their memristor circuits with models that don’t take into account high dynamics of the cells at all. These circuits will never work.” said Stephan Menzel, who leads modeling activities at the Peter Grünberg Institute and has developed powerful compact models that are now in the public domain (www.emrl.de/jart.html). “In our review article, we provide the basics that are extremely helpful for a correct use of our compact models.”

Roadmap neuromorphic computing

The “Roadmap of Neuromorphic Computing and Engineering”, which was published in May 2022, shows how neuromorphic computing can help to reduce the enormous energy consumption of IT globally. In it, researchers from the Peter Grünberg Institute (PGI-7), together with leading experts in the field, have compiled the various technological possibilities, computational approaches, learning algorithms and fields of application. 

According to the study, applications in the field of artificial intelligence, such as pattern recognition or speech recognition, are likely to benefit in a special way from the use of neuromorphic hardware. This is because they are based – much more so than classical numerical computing operations – on the shifting of large amounts of data. Memristive cells make it possible to process these gigantic data sets directly in memory without transporting them back and forth between processor and memory. This could reduce the energy efficiency of artificial neural networks by orders of magnitude.

Memristive cells can also be interconnected to form high-density matrices that enable neural networks to learn locally. This so-called edge computing thus shifts computations from the data center to the factory floor, the vehicle, or the home of people in need of care. Thus, monitoring and controlling processes or initiating rescue measures can be done without sending data via a cloud. “This achieves two things at the same time: you save energy, and at the same time, personal data and data relevant to security remain on site,” says Prof. Dittmann, who played a key role in creating the roadmap as editor.

Here’s a link to and a citation for the ‘roadmap’,

2022 roadmap on neuromorphic computing and engineering by Dennis V Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J Quill, Scott T Keene, Alberto Salleo, Julie Grollier, Danijela Marković, Alice Mizrahi, Peng Yao, J Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H P Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A Cleland, Christoph Posch, ShihChii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini and N Pryds. Neuromorphic Computing and Engineering , Volume 2, Number 2 DOI: 10.1088/2634-4386/ac4a83 20 May 2022 • © 2022 The Author(s)

This paper is open access.

Here’s the most recent paper,

Nanoionic memristive phenomena in metal oxides: the valence change mechanism by Regina Dittmann, Stephan Menzel & Rainer Waser. Advances in Physics
Volume 70, 2021 – Issue 2 Pages 155-349 DOI: https://doi.org/10.1080/00018732.2022.2084006 Published online: 06 Aug 2022

This paper is behind a paywall.

Memristive forming strategy

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This is highly technical and it’s here since I’m informally collecting all the research that I stumble across concerning memristors and neuromorphic engineering.

From a Sept. 5, 2022 news item on Nanowerk, Note: A link has been removed,

The silicon-based CMOS [complementary metal-oxide-semiconductor] technology is fast approaching its physical limits, and the electronics industry is urgently calling for new techniques to keep the long-term development. Two-dimensional (2D) semiconductors, like transition-metal dichalcogenides (TMDs), have become a competitive alternative to traditional semiconducting materials in the post-Moore era, and caused worldwide interest. However, before they can be used in practical applications, some key obstacles must be resolved. One of them is the large electrical contact resistances at the metal-semiconductor interfaces.

The large contact resistances mainly come from two aspects: the high tunneling barrier caused by the wide van der Waals (vdW) gap between the 2D material and the metal electrode; the high Schottky barrier accompanied by strong Fermi level pinning at the metal-semiconductor interface.

Four strategies including edge contact, doping TMDs, phase engineering, and using special metals, have been developed to address this problem. However, they all have shortcomings.

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In a new work (Nano Letters, “Van der Waals Epitaxy and Photoresponse of Hexagonal Tellurium Nanoplates on Flexible Mica Sheets”) coming out of Zhenxing Wang’s group at the National Center for Nanoscience and Technology [located in Beijing, China], the researchers have proposed a brand-new contact resistance lowering strategy of 2D semiconductors with a good feasibility, a wide generality and a high stability.

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You can fill in the blanks at Nanowerk or there’s this link and citation for the paper

Van der Waals Epitaxy and Photoresponse of Hexagonal Tellurium Nanoplates on Flexible Mica Sheets by Qisheng Wang, Muhammad Safdar, Kai Xu, Misbah Mirza, Zhenxing Wang, and Jun He. ACS Nano 2014, 8, 7, 7497–7505 DOI: https://doi.org/10.1021/nn5028104 Publication Date:July 2, 2014 Copyright © 2014 American Chemical Society

This paper is behind a paywall.

Reconfiguring a LEGO-like AI chip with light

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MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing elements that can communicate with each other. Credit: Figure courtesy of the researchers and edited by MIT News

This image certainly challenges any ideas I have about what Lego looks like. It seems they see things differently at the Massachusetts Institute of Technology (MIT). From a June 13, 2022 MIT news release (also on EurekAlert),

Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don’t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device’s internal chip — like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste. 

Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip.

The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip’s layers to communicate optically. Other modular chip designs employ conventional wiring to relay signals between layers. Such intricate connections are difficult if not impossible to sever and rewire, making such stackable designs not reconfigurable.

The MIT design uses light, rather than physical wires, to transmit information through the chip. The chip can therefore be reconfigured, with layers that can be swapped out or stacked on, for instance to add new sensors or updated processors.

“You can add as many computing layers and sensors as you want, such as for light, pressure, and even smell,” says MIT postdoc Jihoon Kang. “We call this a LEGO-like reconfigurable AI chip because it has unlimited expandability depending on the combination of layers.”

The researchers are eager to apply the design to edge computing devices — self-sufficient sensors and other electronics that work independently from any central or distributed resources such as supercomputers or cloud-based computing.

“As we enter the era of the internet of things based on sensor networks, demand for multifunctioning edge-computing devices will expand dramatically,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Our proposed hardware architecture will provide high versatility of edge computing in the future.”

The team’s results are published today in Nature Electronics. In addition to Kim and Kang, MIT authors include co-first authors Chanyeol Choi, Hyunseok Kim, and Min-Kyu Song, and contributing authors Hanwool Yeon, Celesta Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Subeen Pang, Sang-Hoon Bae, Hun S. Kum, and Peng Lin, along with collaborators from Harvard University, Tsinghua University, Zhejiang University, and elsewhere.

Lighting the way

The team’s design is currently configured to carry out basic image-recognition tasks. It does so via a layering of image sensors, LEDs, and processors made from artificial synapses — arrays of memory resistors, or “memristors,” that the team previously developed, which together function as a physical neural network, or “brain-on-a-chip.” Each array can be trained to process and classify signals directly on a chip, without the need for external software or an Internet connection.

In their new chip design, the researchers paired image sensors with artificial synapse arrays, each of which they trained to recognize certain letters — in this case, M, I, and T. While a conventional approach would be to relay a sensor’s signals to a processor via physical wires, the team instead fabricated an optical system between each sensor and artificial synapse array to enable communication between the layers, without requiring a physical connection. 

“Other chips are physically wired through metal, which makes them hard to rewire and redesign, so you’d need to make a new chip if you wanted to add any new function,” says MIT postdoc Hyunseok Kim. “We replaced that physical wire connection with an optical communication system, which gives us the freedom to stack and add chips the way we want.”

The team’s optical communication system consists of paired photodetectors and LEDs, each patterned with tiny pixels. Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data to the next layer. As a signal (for instance an image of a letter) reaches the image sensor, the image’s light pattern encodes a certain configuration of LED pixels, which in turn stimulates another layer of photodetectors, along with an artificial synapse array, which classifies the signal based on the pattern and strength of the incoming LED light.

Stacking up

The team fabricated a single chip, with a computing core measuring about 4 square millimeters, or about the size of a piece of confetti. The chip is stacked with three image recognition “blocks,” each comprising an image sensor, optical communication layer, and artificial synapse array for classifying one of three letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and measured the electrical current that each neural network array produced in response. (The larger the current, the larger the chance that the image is indeed the letter that the particular array is trained to recognize.)

The team found that the chip correctly classified clear images of each letter, but it was less able to distinguish between blurry images, for instance between I and T. However, the researchers were able to quickly swap out the chip’s processing layer for a better “denoising” processor, and found the chip then accurately identified the images.

“We showed stackability, replaceability, and the ability to insert a new function into the chip,” notes MIT postdoc Min-Kyu Song.

The researchers plan to add more sensing and processing capabilities to the chip, and they envision the applications to be boundless.

“We can add layers to a cellphone’s camera so it could recognize more complex images, or makes these into healthcare monitors that can be embedded in wearable electronic skin,” offers Choi, who along with Kim previously developed a “smart” skin for monitoring vital signs.

Another idea, he adds, is for modular chips, built into electronics, that consumers can choose to build up with the latest sensor and processor “bricks.”

“We can make a general chip platform, and each layer could be sold separately like a video game,” Jeehwan Kim says. “We could make different types of neural networks, like for image or voice recognition, and let the customer choose what they want, and add to an existing chip like a LEGO.”

This research was supported, in part, by the Ministry of Trade, Industry, and Energy (MOTIE) from South Korea; the Korea Institute of Science and Technology (KIST); and the Samsung Global Research Outreach Program.

Here’s a link to and a citation for the paper,

Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence by Chanyeol Choi, Hyunseok Kim, Ji-Hoon Kang, Min-Kyu Song, Hanwool Yeon, Celesta S. Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Jaeyong Lee, Ikbeom Jang, Subeen Pang, Kanghyun Ryu, Sang-Hoon Bae, Yifan Nie, Hyun S. Kum, Min-Chul Park, Suyoun Lee, Hyung-Jun Kim, Huaqiang Wu, Peng Lin & Jeehwan Kim. Nature Electronics volume 5, pages 386–393 (2022) 05 May 2022 Issue Date: June 2022 Published: 13 June 2022 DOI: https://doi.org/10.1038/s41928-022-00778-y

This paper is behind a paywall.

FrogHeart’s 2022 comes to an end as 2023 comes into view

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I look forward to 2023 and hope it will be as stimulating as 2022 proved to be. Here’s an overview of the year that was on this blog:

Sounds of science

It seems 2022 was the year that science discovered the importance of sound and the possibilities of data sonification. Neither is new but this year seemed to signal a surge of interest or maybe I just happened to stumble onto more of the stories than usual.

This is not an exhaustive list, you can check out my ‘Music’ category for more here. I have tried to include audio files with the postings but it all depends on how accessible the researchers have made them.

Aliens on earth: machinic biology and/or biological machinery?

When I first started following stories in 2008 (?) about technology or machinery being integrated with the human body, it was mostly about assistive technologies such as neuroprosthetics. You’ll find most of this year’s material in the ‘Human Enhancement’ category or you can search the tag ‘machine/flesh’.

However, the line between biology and machine became a bit more blurry for me this year. You can see what’s happening in the titles listed below (you may recognize the zenobot story; there was an earlier version of xenobots featured here in 2021):

This was the story that shook me,

Are the aliens going to come from outer space or are we becoming the aliens?

Brains (biological and otherwise), AI, & our latest age of anxiety

As we integrate machines into our bodies, including our brains, there are new issues to consider:

  • Going blind when your neural implant company flirts with bankruptcy (long read) April 5, 2022 posting
  • US National Academies Sept. 22-23, 2022 workshop on techno, legal & ethical issues of brain-machine interfaces (BMIs) September 21, 2022 posting

I hope the US National Academies issues a report on their “Brain-Machine and Related Neural Interface Technologies: Scientific, Technical, Ethical, and Regulatory Issues – A Workshop” for 2023.

Meanwhile the race to create brainlike computers continues and I have a number of posts which can be found under the category of ‘neuromorphic engineering’ or you can use these search terms ‘brainlike computing’ and ‘memristors’.

On the artificial intelligence (AI) side of things, I finally broke down and added an ‘artificial intelligence (AI) category to this blog sometime between May and August 2021. Previously, I had used the ‘robots’ category as a catchall. There are other stories but these ones feature public engagement and policy (btw, it’s a Canadian Science Policy Centre event), respectively,

  • “The “We are AI” series gives citizens a primer on AI” March 23, 2022 posting
  • “Age of AI and Big Data – Impact on Justice, Human Rights and Privacy Zoom event on September 28, 2022 at 12 – 1:30 pm EDT” September 16, 2022 posting

These stories feature problems, which aren’t new but seem to be getting more attention,

While there have been issues over AI, the arts, and creativity previously, this year they sprang into high relief. The list starts with my two-part review of the Vancouver Art Gallery’s AI show; I share most of my concerns in part two. The third post covers intellectual property issues (mostly visual arts but literary arts get a nod too). The fourth post upends the discussion,

  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (1 of 2): The Objects” July 28, 2022 posting
  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (2 of 2): Meditations” July 28, 2022 posting
  • “AI (artificial intelligence) and art ethics: a debate + a Botto (AI artist) October 2022 exhibition in the Uk” October 24, 2022 posting
  • Should AI algorithms get patents for their inventions and is anyone talking about copyright for texts written by AI algorithms? August 30, 2022 posting

Interestingly, most of the concerns seem to be coming from the visual and literary arts communities; I haven’t come across major concerns from the music community. (The curious can check out Vancouver’s Metacreation Lab for Artificial Intelligence [located on a Simon Fraser University campus]. I haven’t seen any cautionary or warning essays there; it’s run by an AI and creativity enthusiast [professor Philippe Pasquier]. The dominant but not sole focus is art, i.e., music and AI.)

There is a ‘new kid on the block’ which has been attracting a lot of attention this month. If you’re curious about the latest and greatest AI anxiety,

  • Peter Csathy’s December 21, 2022 Yahoo News article (originally published in The WRAP) makes this proclamation in the headline “Chat GPT Proves That AI Could Be a Major Threat to Hollywood Creatives – and Not Just Below the Line | PRO Insight”
  • Mouhamad Rachini’s December 15, 2022 article for the Canadian Broadcasting Corporation’s (CBC) online news overs a more generalized overview of the ‘new kid’ along with an embedded CBC Radio file which runs approximately 19 mins. 30 secs. It’s titled “ChatGPT a ‘landmark event’ for AI, but what does it mean for the future of human labour and disinformation?” The chat bot’s developer, OpenAI, has been mentioned here many times including the previously listed July 28, 2022 posting (part two of the VAG review) and the October 24, 2022 posting.

Opposite world (quantum physics in Canada)

Quantum computing made more of an impact here (my blog) than usual. it started in 2021 with the announcement of a National Quantum Strategy in the Canadian federal government budget for that year and gained some momentum in 2022:

  • “Quantum Mechanics & Gravity conference (August 15 – 19, 2022) launches Vancouver (Canada)-based Quantum Gravity Institute and more” July 26, 2022 posting Note: This turned into one of my ‘in depth’ pieces where I comment on the ‘Canadian quantum scene’ and highlight the appointment of an expert panel for the Council of Canada Academies’ report on Quantum Technologies.
  • “Bank of Canada and Multiverse Computing model complex networks & cryptocurrencies with quantum computing” July 25, 2022 posting
  • “Canada, quantum technology, and a public relations campaign?” December 29, 2022 posting

This one was a bit of a puzzle with regard to placement in this end-of-year review, it’s quantum but it’s also about brainlike computing

It’s getting hot in here

Fusion energy made some news this year.

There’s a Vancouver area company, General Fusion, highlighted in both postings and the October posting includes an embedded video of Canadian-born rapper Baba Brinkman’s “You Must LENR” [L ow E nergy N uclear R eactions or sometimes L attice E nabled N anoscale R eactions or Cold Fusion or CANR (C hemically A ssisted N uclear R eactions)].

BTW, fusion energy can generate temperatures up to 150 million degrees Celsius.

Ukraine, science, war, and unintended consequences

Here’s what you might expect,

These are the unintended consequences (from Rachel Kyte’s, Dean of the Fletcher School, Tufts University, December 26, 2022 essay on The Conversation [h/t December 27, 2022 news item on phys.org]), Note: Links have been removed,

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Russian President Vladimir Putin’s war on Ukraine has reverberated through Europe and spread to other countries that have long been dependent on the region for natural gas. But while oil-producing countries and gas lobbyists are arguing for more drilling, global energy investments reflect a quickening transition to cleaner energy. [emphasis mine]

Call it the Putin effect – Russia’s war is speeding up the global shift away from fossil fuels.

In December [2022?], the International Energy Agency [IEA] published two important reports that point to the future of renewable energy.

First, the IEA revised its projection of renewable energy growth upward by 30%. It now expects the world to install as much solar and wind power in the next five years as it installed in the past 50 years.

The second report showed that energy use is becoming more efficient globally, with efficiency increasing by about 2% per year. As energy analyst Kingsmill Bond at the energy research group RMI noted, the two reports together suggest that fossil fuel demand may have peaked. While some low-income countries have been eager for deals to tap their fossil fuel resources, the IEA warns that new fossil fuel production risks becoming stranded, or uneconomic, in the next 20 years.

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Kyte’s essay is not all ‘sweetness and light’ but it does provide a little optimism.

Kudos, nanotechnology, culture (pop & otherwise), fun, and a farewell in 2022

This one was a surprise for me,

Sometimes I like to know where the money comes from and I was delighted to learn of the Ărramăt Project funded through the federal government’s New Frontiers in Research Fund (NFRF). Here’s more about the Ărramăt Project from the February 14, 2022 posting,

…

“The Ărramăt Project is about respecting the inherent dignity and interconnectedness of peoples and Mother Earth, life and livelihood, identity and expression, biodiversity and sustainability, and stewardship and well-being. Arramăt is a word from the Tamasheq language spoken by the Tuareg people of the Sahel and Sahara regions which reflects this holistic worldview.” (Mariam Wallet Aboubakrine)

Over 150 Indigenous organizations, universities, and other partners will work together to highlight the complex problems of biodiversity loss and its implications for health and well-being. The project Team will take a broad approach and be inclusive of many different worldviews and methods for research (i.e., intersectionality, interdisciplinary, transdisciplinary). Activities will occur in 70 different kinds of ecosystems that are also spiritually, culturally, and economically important to Indigenous Peoples.

The project is led by Indigenous scholars and activists …

Kudos to the federal government and all those involved in the Salmon science camps, the Ărramăt Project, and other NFRF projects.

There are many other nanotechnology posts here but this appeals to my need for something lighter at this point,

  • “Say goodbye to crunchy (ice crystal-laden) in ice cream thanks to cellulose nanocrystals (CNC)” August 22, 2022 posting

The following posts tend to be culture-related, high and/or low but always with a science/nanotechnology edge,

Sadly, it looks like 2022 is the last year that Ada Lovelace Day is to be celebrated.

… this year’s Ada Lovelace Day is the final such event due to lack of financial backing. Suw Charman-Anderson told the BBC [British Broadcasting Corporation] the reason it was now coming to an end was:

You can read more about it here:

In the rearview mirror

A few things that didn’t fit under the previous heads but stood out for me this year. Science podcasts, which were a big feature in 2021, also proliferated in 2022. I think they might have peaked and now (in 2023) we’ll see what survives.

Nanotechnology, the main subject on this blog, continues to be investigated and increasingly integrated into products. You can search the ‘nanotechnology’ category here for posts of interest something I just tried. It surprises even me (I should know better) how broadly nanotechnology is researched and applied.

If you want a nice tidy list, Hamish Johnston in a December 29, 2022 posting on the Physics World Materials blog has this “Materials and nanotechnology: our favourite research in 2022,” Note: Links have been removed,

“Inherited nanobionics” makes its debut

The integration of nanomaterials with living organisms is a hot topic, which is why this research on “inherited nanobionics” is on our list. Ardemis Boghossian at EPFL [École polytechnique fédérale de Lausanne] in Switzerland and colleagues have shown that certain bacteria will take up single-walled carbon nanotubes (SWCNTs). What is more, when the bacteria cells split, the SWCNTs are distributed amongst the daughter cells. The team also found that bacteria containing SWCNTs produce a significantly more electricity when illuminated with light than do bacteria without nanotubes. As a result, the technique could be used to grow living solar cells, which as well as generating clean energy, also have a negative carbon footprint when it comes to manufacturing.

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Getting to back to Canada, I’m finding Saskatchewan featured more prominently here. They do a good job of promoting their science, especially the folks at the Canadian Light Source (CLS), Canada’s synchrotron, in Saskatoon. Canadian live science outreach events seeming to be coming back (slowly). Cautious organizers (who have a few dollars to spare) are also enthusiastic about hybrid events which combine online and live outreach.

After what seems like a long pause, I’m stumbling across more international news, e.g. “Nigeria and its nanotechnology research” published December 19, 2022 and “China and nanotechnology” published September 6, 2022. I think there’s also an Iran piece here somewhere.

With that …

Making resolutions in the dark

Hopefully this year I will catch up with the Council of Canadian Academies (CCA) output and finally review a few of their 2021 reports such as Leaps and Boundaries; a report on artificial intelligence applied to science inquiry and, perhaps, Powering Discovery; a report on research funding and Natural Sciences and Engineering Research Council of Canada.

Given what appears to a renewed campaign to have germline editing (gene editing which affects all of your descendants) approved in Canada, I might even reach back to a late 2020 CCA report, Research to Reality; somatic gene and engineered cell therapies. it’s not the same as germline editing but gene editing exists on a continuum.

For anyone who wants to see the CCA reports for themselves they can be found here (both in progress and completed).

I’m also going to be paying more attention to how public relations and special interests influence what science is covered and how it’s covered. In doing this 2022 roundup, I noticed that I featured an overview of fusion energy not long before the breakthrough. Indirect influence on this blog?

My post was precipitated by an article by Alex Pasternak in Fast Company. I’m wondering what precipitated Alex Pasternack’s interest in fusion energy since his self-description on the Huffington Post website states this “… focus on the intersections of science, technology, media, politics, and culture. My writing about those and other topics—transportation, design, media, architecture, environment, psychology, art, music … .”

He might simply have received a press release that stimulated his imagination and/or been approached by a communications specialist or publicists with an idea. There’s a reason for why there are so many public relations/media relations jobs and agencies.

Que sera, sera (Whatever will be, will be)

I can confidently predict that 2023 has some surprises in store. I can also confidently predict that the European Union’s big research projects (1B Euros each in funding for the Graphene Flagship and Human Brain Project over a ten year period) will sunset in 2023, ten years after they were first announced in 2013. Unless, the powers that be extend the funding past 2023.

I expect the Canadian quantum community to provide more fodder for me in the form of a 2023 report on Quantum Technologies from the Council of Canadian academies, if nothing else otherwise.

I’ve already featured these 2023 science events but just in case you missed them,

  • 2023 Preview: Bill Nye the Science Guy’s live show and Marvel Avengers S.T.A.T.I.O.N. (Scientific Training And Tactical Intelligence Operative Network) coming to Vancouver (Canada) November 24, 2022 posting
  • September 2023: Auckland, Aotearoa New Zealand set to welcome women in STEM (science, technology, engineering, and mathematics) November 15, 2022 posting

Getting back to this blog, it may not seem like a new year during the first few weeks of 2023 as I have quite the stockpile of draft posts. At this point I have drafts that are dated from June 2022 and expect to be burning through them so as not to fall further behind but will be interspersing them, occasionally, with more current posts.

Most importantly: a big thank you to everyone who drops by and reads (and sometimes even comments) on my posts!!! it’s very much appreciated and on that note: I wish you all the best for 2023.

Swiss researchers, memristors, perovskite crystals, and neuromorphic (brainlike) computing

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A May 18, 2022 news item on Nanowerk highlights research into making memristors more ‘flexible’, (Note: There’s an almost identical May 18, 2022 news item on ScienceDaily but the issuing agency is listed as ETH Zurich rather than Empa as listed on Nanowerk),

Compared with computers, the human brain is incredibly energy-efficient. Scientists are therefore drawing on how the brain and its interconnected neurons function for inspiration in designing innovative computing technologies. They foresee that these brain-inspired computing systems, will be more energy-efficient than conventional ones, as well as better at performing machine-learning tasks.

Much like neurons, which are responsible for both data storage and data processing in the brain, scientists want to combine storage and processing in a single type of electronic component, known as a memristor. Their hope is that this will help to achieve greater efficiency because moving data between the processor and the storage, as conventional computers do, is the main reason for the high energy consumption in machine-learning applications.

Researchers at ETH Zurich, Empa and the University of Zurich have now developed an innovative concept for a memristor that can be used in a far wider range of applications than existing memristors.

“There are different operation modes for memristors, and it is advantageous to be able to use all these modes depending on an artificial neural network’s architecture,” explains ETH Zurich postdoc Rohit John. “But previous conventional memristors had to be configured for one of these modes in advance.”

The new memristors can now easily switch between two operation modes while in use: a mode in which the signal grows weaker over time and dies (volatile mode), and one in which the signal remains constant (non-volatile mode).

…

Once you get past the first two paragraphs in the Nanowerk news item, you find the ETH Zurich and Empa May 18, 2022 press releases by Fabio Begamin, in both cases, are identical (ETH is listed as the authoring agency on EurekAlert), (Note: A link has been removed in the following),

Just like in the brain

“These two operation modes are also found in the human brain,” John says. On the one hand, stimuli at the synapses are transmitted from neuron to neuron with biochemical neurotransmitters. These stimuli start out strong and then gradually become weaker. On the other hand, new synaptic connections to other neurons form in the brain while we learn. These connections are longer-​lasting.

John, who is a postdoc in the group headed by ETH Professor Maksym Kovalenko, was awarded an ETH fellowship for outstanding postdoctoral researchers in 2020. John conducted this research together with Yiğit Demirağ, a doctoral student in Professor Giacomo Indiveri’s group at the Institute for Neuroinformatics of the University of Zurich and ETH Zurich.

Semiconductor known from solar cells

The memristors the researchers have developed are made of halide perovskite nanocrystals, a semiconductor material known primarily from its use in photovoltaic cells. “The ‘nerve conduction’ in these new memristors is mediated by temporarily or permanently stringing together silver ions from an electrode to form a nanofilament penetrating the perovskite structure through which current can flow,” explains Kovalenko.

This process can be regulated to make the silver-​ion filament either thin, so that it gradually breaks back down into individual silver ions (volatile mode), or thick and permanent (non-​volatile mode). This is controlled by the intensity of the current conducted on the memristor: applying a weak current activates the volatile mode, while a strong current activates the non-​volatile mode.

New toolkit for neuroinformaticians

“To our knowledge, this is the first memristor that can be reliably switched between volatile and non-​volatile modes on demand,” Demirağ says. This means that in the future, computer chips can be manufactured with memristors that enable both modes. This is a significance advance because it is usually not possible to combine several different types of memristors on one chip.

Within the scope of the study, which they published in the journal Nature Communications, the researchers tested 25 of these new memristors and carried out 20,000 measurements with them. In this way, they were able to simulate a computational problem on a complex network. The problem involved classifying a number of different neuron spikes as one of four predefined patterns.

Before these memristors can be used in computer technology, they will need to undergo further optimisation.  However, such components are also important for research in neuroinformatics, as Indiveri points out: “These components come closer to real neurons than previous ones. As a result, they help researchers to better test hypotheses in neuroinformatics and hopefully gain a better understanding of the computing principles of real neuronal circuits in humans and animals.”

Here’s a link to and a citation for the paper,

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing by Rohit Abraham John, Yiğit Demirağ, Yevhen Shynkarenko, Yuliia Berezovska, Natacha Ohannessian, Melika Payvand, Peng Zeng, Maryna I. Bodnarchuk, Frank Krumeich, Gökhan Kara, Ivan Shorubalko, Manu V. Nair, Graham A. Cooke, Thomas Lippert, Giacomo Indiveri & Maksym V. Kovalenko. Nature Communications volume 13, Article number: 2074 (2022) DOI: https://doi.org/10.1038/s41467-022-29727-1 Published: 19 April 2022

This paper is open access.

Quantum memristors

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This March 24, 2022 news item on Nanowerk announcing work on a quantum memristor seems to have had a rough translation from German to English,

In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable of computational power well beyond the reach of even the world’s largest supercomputer.

Physicists at the University of Vienna have now demonstrated a new device, called quantum memristor, which may allow to combine these two worlds, thus unlocking unprecedented capabilities. The experiment, carried out in collaboration with the National Research Council (CNR) and the Politecnico di Milano in Italy, has been realized on an integrated quantum processor operating on single photons.

…

Caption: Abstract representation of a neural network which is made of photons and has memory capability potentially related to artificial intelligence. Credit: © Equinox Graphics, University of Vienna

A March 24, 2022 University of Vienna (Universität Wien) press release (also on EurekAlert), which originated the news item, explains why this work has an impact on artificial intelligence,

At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.

One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.

A group of experimental physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano led by Prof. Philip Walther and Dr. Roberto Osellame, have now demonstrated that it is possible to engineer a device that has the same behavior as a memristor, while acting on quantum states and being able to encode and transmit quantum information. In other words, a quantum memristor. Realizing such device is challenging because the dynamics of a memristor tends to contradict the typical quantum behavior. 

By using single photons, i.e. single quantum particles of lights, and exploiting their unique ability to propagate simultaneously in a superposition of two or more paths, the physicists have overcome the challenge. In their experiment, single photons propagate along waveguides laser-written on a glass substrate and are guided on a superposition of several paths. One of these paths is used to measure the flux of photons going through the device and this quantity, through a complex electronic feedback scheme, modulates the transmission on the other output, thus achieving the desired memristive behavior. Besides demonstrating the quantum memristor, the researchers have provided simulations showing that optical networks with quantum memristor can be used to learn on both classical and quantum tasks, hinting at the fact that the quantum memristor may be the missing link between artificial intelligence and quantum computing.

“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges of the current research in quantum physics and computer science”, says Michele Spagnolo, who is first author of the publication in the journal “Nature Photonics”. The group of Philip Walther of the University of Vienna has also recently demonstrated that robots can learn faster when using quantum resources and borrowing schemes from quantum computation. This new achievement represents one more step towards a future where quantum artificial intelligence become reality.

Here’s a link to and a citation for the paper,

Experimental photonic quantum memristor by Michele Spagnolo, Joshua Morris, Simone Piacentini, Michael Antesberger, Francesco Massa, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame & Philip Walther. Nature Photonics volume 16, pages 318–323 (2022) DOI: https://doi.org/10.1038/s41566-022-00973-5 Published 24 March 2022 Issue Date April 2022

This paper is open access.

Honey-based neuromorphic chips for brainlike computers?

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Photo by Mariana Ibanez on Unsplash Courtesy Washington State University

An April 5, 2022 news item on Nanowerk explains the connection between honey and a neuromorphic (brainlike) computer chip, Note: Links have been removed,

Honey might be a sweet solution for developing environmentally friendly components for neuromorphic computers, systems designed to mimic the neurons and synapses found in the human brain.

Hailed by some as the future of computing, neuromorphic systems are much faster and use much less power than traditional computers. Washington State University engineers have demonstrated one way to make them more organic too.

In a study published in Journal of Physics D (“Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems”), the researchers show that honey can be used to make a memristor, a component similar to a transistor that can not only process but also store data in memory.

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An April 5, 2022 Washington State University (WSU) news release (also on EurekAlert) by Sara Zaske, which originated the news item, describes the purpose for the work and details about making chips from honey,

“This is a very small device with a simple structure, but it has very similar functionalities to a human neuron,” said Feng Zhao, associate professor of WSU’s School of Engineering and Computer Science and corresponding author on the study.“This means if we can integrate millions or billions of these honey memristors together, then they can be made into a neuromorphic system that functions much like a human brain.”

For the study, Zhao and first author Brandon Sueoka, a WSU graduate student in Zhao’s lab, created memristors by processing honey into a solid form and sandwiching it between two metal electrodes, making a structure similar to a human synapse. They then tested the honey memristors’ ability to mimic the work of synapses with high switching on and off speeds of 100 and 500 nanoseconds respectively. The memristors also emulated the synapse functions known as spike-timing dependent plasticity and spike-rate dependent plasticity, which are responsible for learning processes in human brains and retaining new information in neurons.

The WSU engineers created the honey memristors on a micro-scale, so they are about the size of a human hair. The research team led by Zhao plans to develop them on a nanoscale, about 1/1000 of a human hair, and bundle many millions or even billions together to make a full neuromorphic computing system.

Currently, conventional computer systems are based on what’s called the von Neumann architecture. Named after its creator, this architecture involves an input, usually from a keyboard and mouse, and an output, such as the monitor. It also has a CPU, or central processing unit, and RAM, or memory storage. Transferring data through all these mechanisms from input to processing to memory to output takes a lot of power at least compared to the human brain, Zhao said. For instance, the Fugaku supercomputer uses upwards of 28 megawatts, roughly equivalent to 28 million watts, to run while the brain uses only around 10 to 20 watts.

The human brain has more than 100 billion neurons with more than 1,000 trillion synapses, or connections, among them. Each neuron can both process and store data, which makes the brain much more efficient than a traditional computer, and developers of neuromorphic computing systems aim to mimic that structure.

Several companies, including Intel and IBM, have released neuromorphic chips which have the equivalent of more than 100 million “neurons” per chip, but this is not yet near the number in the brain. Many developers are also still using the same nonrenewable and toxic materials that are currently used in conventional computer chips.

Many researchers, including Zhao’s team, are searching for biodegradable and renewable solutions for use in this promising new type of computing. Zhao is also leading investigations into using proteins and other sugars such as those found in Aloe vera leaves in this capacity, but he sees strong potential in honey.

“Honey does not spoil,” he said. “It has a very low moisture concentration, so bacteria cannot survive in it. This means these computer chips will be very stable and reliable for a very long time.”

The honey memristor chips developed at WSU should tolerate the lower levels of heat generated by neuromorphic systems which do not get as hot as traditional computers. The honey memristors will also cut down on electronic waste.

“When we want to dispose of devices using computer chips made of honey, we can easily dissolve them in water,” he said. “Because of these special properties, honey is very useful for creating renewable and biodegradable neuromorphic systems.”

This also means, Zhao cautioned, that just like conventional computers, users will still have to avoid spilling their coffee on them.

Nice note of humour at the end. There are a few questions, I wonder if the variety of honey (clover, orange blossom, blackberry, etc.) has an impact on the chip’s speed and/or longevity. Also, if someone spilled coffee and the chip melted and a child decided to lap it up, what would happen?

Here’s a link to and a citation for the paper,

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems. Brandon Sueoka and Feng Zhao. Journal of Physics D: Applied Physics, Volume 55, Number 22 (225105) Published 7 March 2022 • © 2022 IOP Publishing Ltd

This paper is behind a paywall.

Device with brainlike plasticity

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A September 1, 2021 news item on ScienceDaily announces a new type of memristor from Texas A&M University (Texas A&M or TAMU) and the National University of Singapore (NUS)

In a discovery published in the journal Nature, an international team of researchers has described a novel molecular device with exceptional computing prowess.

Reminiscent of the plasticity of connections in the human brain, the device can be reconfigured on the fly for different computational tasks by simply changing applied voltages. Furthermore, like nerve cells can store memories, the same device can also retain information for future retrieval and processing.

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Two of the universities involved in the research have issued news/press releases. I’m going to start with the September 1, 2021 Texas A&M University news release (also on EurekAlert), which originated the news item on ScienceDaily,

“The brain has the remarkable ability to change its wiring around by making and breaking connections between nerve cells. Achieving something comparable in a physical system has been extremely challenging,” said Dr. R. Stanley Williams [emphasis mine], professor in the Department of Electrical and Computer Engineering at Texas A&M University. “We have now created a molecular device with dramatic reconfigurability, which is achieved not by changing physical connections like in the brain, but by reprogramming its logic.”

Dr. T. Venkatesan, director of the Center for Quantum Research and Technology (CQRT) at the University of Oklahoma, Scientific Affiliate at National Institute of Standards and Technology, Gaithersburg, and adjunct professor of electrical and computer engineering at the National University of Singapore, added that their molecular device might in the future help design next-generation processing chips with enhanced computational power and speed, but consuming significantly reduced energy.

Whether it is the familiar laptop or a sophisticated supercomputer, digital technologies face a common nemesis, the von Neumann bottleneck. This delay in computational processing is a consequence of current computer architectures, wherein the memory, containing data and programs, is physically separated from the processor. As a result, computers spend a significant amount of time shuttling information between the two systems, causing the bottleneck. Also, despite extremely fast processor speeds, these units can be idling for extended amounts of time during periods of information exchange.

As an alternative to conventional electronic parts used for designing memory units and processors, devices called memristors offer a way to circumvent the von Neumann bottleneck. Memristors, such as those made of niobium dioxide and vanadium dioxide, transition from being an insulator to a conductor at a set temperature. This property gives these types of memristors the ability to perform computations and store data.

However, despite their many advantages, these metal oxide memristors are made of rare-earth elements and can operate only in restrictive temperature regimes. Hence, there has been an ongoing search for promising organic molecules that can perform a comparable memristive function, said Williams.

Dr. Sreebrata Goswami, a professor at the Indian Association for the Cultivation of Science, designed the material used in this work. The compound has a central metal atom (iron) bound to three phenyl azo pyridine organic molecules called ligands.

“This behaves like an electron sponge that can absorb as many as six electrons reversibly, resulting in seven different redox states,” said Sreebrata. “The interconnectivity between these states is the key behind the reconfigurability shown in this work.”

Dr. Sreetosh Goswami, a researcher at the National University of Singapore, devised this project by creating a tiny electrical circuit consisting of a 40-nanometer layer of molecular film sandwiched between a layer of gold on top and gold-infused nanodisc and indium tin oxide at the bottom.

On applying a negative voltage on the device, Sreetosh witnessed a current-voltage profile that was nothing like anyone had seen before. Unlike metal-oxide memristors that can switch from metal to insulator at only one fixed voltage, the organic molecular devices could switch back and forth from insulator to conductor at several discrete sequential voltages.

“So, if you think of the device as an on-off switch, as we were sweeping the voltage more negative, the device first switched from on to off, then off to on, then on to off and then back to on. I’ll say that we were just blown out of our seat,” said Venkatesan. “We had to convince ourselves that what we were seeing was real.”

Sreetosh and Sreebrata investigated the molecular mechanisms underlying the curious switching behavior using an imaging technique called Raman spectroscopy. In particular, they looked for spectral signatures in the vibrational motion of the organic molecule that could explain the multiple transitions. Their investigation revealed that sweeping the voltage negative triggered the ligands on the molecule to undergo a series of reduction, or electron-gaining, events that caused the molecule to transition between off state and on states.

Next, to describe the extremely complex current-voltage profile of the molecular device mathematically, Williams deviated from the conventional approach of basic physics-based equations. Instead, he described the behavior of the molecules using a decision tree algorithm with “if-then-else” statements, a commonplace line of code in several computer programs, particularly digital games.

“Video games have a structure where you have a character that does something, and then something occurs as a result. And so, if you write that out in a computer algorithm, they are if-then-else statements,” said Williams. “Here, the molecule is switching from on to off as a consequence of applied voltage, and that’s when I had the eureka moment to use decision trees to describe these devices, and it worked very well.” 

But the researchers went a step further to exploit these molecular devices to run programs for different real-world computational tasks. Sreetosh showed experimentally that their devices could perform fairly complex computations in a single time step and then be reprogrammed to perform another task in the next instant.

“It was quite extraordinary; our device was doing something like what the brain does, but in a very different way,” said Sreetosh. “When you’re learning something new or when you’re deciding, the brain can actually reconfigure and change physical wiring around. Similarly, we can logically reprogram or reconfigure our devices by giving them a different voltage pulse then they’ve seen before.” 

Venkatesan noted that it would take thousands of transistors to perform the same computational functions as one of their molecular devices with its different decision trees. Hence, he said their technology might first be used in handheld devices, like cell phones and sensors, and other applications where power is limited.

Other contributors to the research include Dr. Abhijeet Patra and Dr. Ariando from the National University of Singapore; Dr. Rajib Pramanick and Dr. Santi Prasad Rath from the Indian Association for the Cultivation of Science; Dr. Martin Foltin from Hewlett Packard Enterprise, Colorado; and Dr. Damien Thompson from the University of Limerick, Ireland.

Venkatesan said that this research is indicative of the future discoveries from this collaborative team, which will include the center of nanoscience and engineering at the Indian Institute of Science and the Microsystems and Nanotechnology Division at the NIST.

I’ve highlighted R. Stanley Williams because he and his team at HP [Hewlett Packard] Labs helped to kick off current memristor research in 2008 with the publication of two papers as per my April 5, 2010 posting,

In 2008, two memristor papers were published in Nature and Nature Nanotechnology, respectively. In the first (Nature, May 2008 [article still behind a paywall], a team at HP Labs claimed they had proved the existence of memristors (a fourth member of electrical engineering’s ‘Holy Trinity of the capacitor, resistor, and inductor’). In the second paper (Nature Nanotechnology, July 2008 [article still behind a paywall]) the team reported that they had achieved engineering control.

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The novel memory device is based on a molecular system that can transition between on and off states at several discrete sequential voltages Courtesy: National University of Singapore

There is more technical detail in the September 2, 2022 NUS press release (also on EurekAlert),

Many electronic devices today are dependent on semiconductor logic circuits based on switches hard-wired to perform predefined logic functions. Physicists from the National University of Singapore (NUS), together with an international team of researchers, have developed a novel molecular memristor, or an electronic memory device, that has exceptional memory reconfigurability. 

Unlike hard-wired standard circuits, the molecular device can be reconfigured using voltage to embed different computational tasks. The energy-efficient new technology, which is capable of enhanced computational power and speed, can potentially be used in edge computing, as well as handheld devices and applications with limited power resource.

“This work is a significant breakthrough in our quest to design low-energy computing. The idea of using multiple switching in a single element draws inspiration from how the brain works and fundamentally reimagines the design strategy of a logic circuit,” said Associate Professor Ariando from the NUS Department of Physics who led the research.

The research was first published in the journal Nature on 1 September 2021, and carried out in collaboration with the Indian Association for the Cultivation of Science, Hewlett Packard Enterprise, the University of Limerick, the University of Oklahoma, and Texas A&M University.

Brain-inspired technology

“This new discovery can contribute to developments in edge computing as a sophisticated in-memory computing approach to overcome the von Neumann bottleneck, a delay in computational processing seen in many digital technologies due to the physical separation of memory storage from a device’s processor,” said Assoc Prof Ariando. The new molecular device also has the potential to contribute to designing next generation processing chips with enhanced computational power and speed.

“Similar to the flexibility and adaptability of connections in the human brain, our memory device can be reconfigured on the fly for different computational tasks by simply changing applied voltages. Furthermore, like how nerve cells can store memories, the same device can also retain information for future retrieval and processing,” said first author Dr Sreetosh Goswami, Research Fellow from the Department of Physics at NUS.

Research team member Dr Sreebrata Goswami, who was a Senior Research Scientist at NUS and previously Professor at the Indian Association for the Cultivation of Science, conceptualised and designed a molecular system belonging to the chemical family of phenyl azo pyridines that have a central metal atom bound to organic molecules called ligands. “These molecules are like electron sponges that can offer as many as six electron transfers resulting in five different molecular states. The interconnectivity between these states is the key behind the device’s reconfigurability,” explained Dr Sreebrata Goswami.

Dr Sreetosh Goswami created a tiny electrical circuit consisting a 40-nanometer layer of molecular film sandwiched between a top layer of gold, and a bottom layer of gold-infused nanodisc and indium tin oxide. He observed an unprecedented current-voltage profile upon applying a negative voltage to the device. Unlike conventional metal-oxide memristors that are switched on and off at only one fixed voltage, these organic molecular devices could switch between on-off states at several discrete sequential voltages.

Using an imaging technique called Raman spectroscopy, spectral signatures in the vibrational motion of the organic molecule were observed to explain the multiple transitions. Dr Sreebrata Goswami explained, “Sweeping the negative voltage triggered the ligands on the molecule to undergo a series of reduction, or electron-gaining which caused the molecule to transition between off and on states.”

The researchers described the behavior of the molecules using a decision tree algorithm with “if-then-else” statements, which is used in the coding of several computer programs, particularly digital games, as compared to the conventional approach of using basic physics-based equations.

New possibilities for energy-efficient devices

Building on their research, the team used the molecular memory devices to run programs for different real-world computational tasks. As a proof of concept, the team demonstrated that their technology could perform complex computations in a single step, and could be reprogrammed to perform another task in the next instant. An individual molecular memory device could perform the same computational functions as thousands of transistors, making the technology a more powerful and energy-efficient memory option.

“The technology might first be used in handheld devices, like cell phones and sensors, and other applications where power is limited,” added Assoc Prof Ariando.

The team in the midst of building new electronic devices incorporating their innovation, and working with collaborators to conduct simulation and benchmarking relating to existing technologies.

Other contributors to the research paper include Abhijeet Patra and Santi Prasad Rath from NUS, Rajib Pramanick from the Indian Association for the Cultivation of Science, Martin Foltin from Hewlett Packard Enterprise, Damien Thompson from the University of Limerick, T. Venkatesan from the University of Oklahoma, and R. Stanley Williams from Texas A&M University.

Here’s a link to and a citation for the paper,

Decision trees within a molecular memristor by Sreetosh Goswami, Rajib Pramanick, Abhijeet Patra, Santi Prasad Rath, Martin Foltin, A. Ariando, Damien Thompson, T. Venkatesan, Sreebrata Goswami & R. Stanley Williams. Nature volume 597, pages 51–56 (2021) DOI: https://doi.org/10.1038/s41586-021-03748-0 Published 01 September 2021 Issue Date 02 September 2021

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